Nudging Toward Responsible Recommendations: A Graph-Based Approach to Mitigate Belief Filter Bubbles

Mengyan Wang, Yuxuan Hu, Shiqing Wu, Weihua Li, Quan Bai, Zihan Yuan, Chenting Jiang

Abstract: Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. This article presents the responsible graph-based recommendation (RGRec) system, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as an intermediate agency between users and existing preference-based recommendation systems, RGRec is composed of three collaborative modules: the multifaceted reasoning-based filter bubbles detection (FBDetect) module, the belief nudging module, and the generative artificial intelligence (GAI)-based recommendation strategy generation module (RecomGen). The FBDetect module identifies users with extreme belief imbalances based on their belief networks, which are represented as heterogeneous graphs. These graphs are then utilized in the Belief Nudging module, where a nudging strategy is employed to adapt prompts for the RecomGen module. Ultimately, the RecomGen module generates contextually rich items for recommendations. Experimental results demonstrate that RGRec can promote diverse content exploration based on user feedback and progressively stimulate interest in topics users initially showed less interest in, encouraging individual exploration.

External IDs:dblp:journals/tai/WangHWLBYJ25